The increasing installation of Photovoltaics (PV) cells leads to more
generation of renewable energy sources (RES), but results in increased
uncertainties of energy scheduling. Predicting PV power generation is important
for energy management and dispatch optimization in smart grid. However, the PV
power generation data is often collected across different types of customers
(e.g., residential, agricultural, industrial, and commercial) while the
customer information is always de-identified. This often results in a
forecasting model trained with all PV power generation data, allowing the
predictor to learn various patterns through intra-model self-learning, instead
of constructing a separate predictor for each customer type. In this paper, we
propose a clustering-based multitasking deep neural network (CM-DNN) framework
for PV power generation prediction. K-means is applied to cluster the data into
different customer types. For each type, a deep neural network (DNN) is
employed and trained until the accuracy cannot be improved. Subsequently, for a
specified customer type (i.e., the target task), inter-model knowledge transfer
is conducted to enhance its training accuracy. During this process, source task
selection is designed to choose the optimal subset of tasks (excluding the
target customer), and each selected source task uses a coefficient to determine
the amount of DNN model knowledge (weights and biases) transferred to the aimed
prediction task. The proposed CM-DNN is tested on a real-world PV power
generation dataset and its superiority is demonstrated by comparing the
prediction performance on training the dataset with a single model without
clustering.

本文提出了一种基于聚类的多任务深度神经网络（CM-DNN）框架，用于预测光伏发电量。通过应用 K-means 算法将数据聚类为不同的客户类型，并为每个类型采用深度神经网络进行训练，然后进行模型知识传输以提高目标任务的训练准确性。通过对真实世界光伏发电数据集的测试，证明了该方法在预测性能上优于无聚类的单一模型。

基于聚类的多任务深度神经网络用于太阳能光伏发电预测

Clustering-based Multitasking Deep Neural Network for Solar  Photovoltaics Power Generation Prediction

Accelerating material discovery holds the potential to greatly help mitigate
the climate crisis. Discovering new solid-state crystals such as
electrocatalysts, ionic conductors or photovoltaics can have a crucial impact,
for instance, in improving the efficiency of renewable energy production and
storage. In this paper, we introduce Crystal-GFlowNet, a generative model of
crystal structures that sequentially samples a crystal's composition, space
group and lattice parameters. This domain-inspired approach enables the
flexible incorporation of physical and geometrical constraints, as well as the
use of any available predictive model of a desired property as an objective
function. We evaluate the capabilities of Crystal-GFlowNet by using as
objective the formation energy of a crystal structure, as predicted by a new
proxy model trained on MatBench. The results demonstrate that Crystal-GFlowNet
is able to sample diverse crystals with low formation energy.

加速材料发现对缓解气候危机具有潜力。我们介绍了一种名为 Crystal-GFlowNet 的晶体结构生成模型，它能够灵活地加入物理和几何约束，并使用预测模型作为目标函数。通过在 MatBench 上训练的代理模型对结晶能进行预测，我们评估了 Crystal-GFlowNet 的能力，结果表明它能够生成结晶能较低的多样化晶体。

Crystal-GFN: 采样具有理想性质和约束条件的晶体

Crystal-GFN: sampling crystals with desirable properties and constraints

The drastic growth of electric vehicles and photovoltaics can introduce new
challenges, such as electrical current congestion and voltage limit violations
due to peak load demands. These issues can be mitigated by controlling the
operation of electric vehicles i.e., smart charging. Centralized smart charging
solutions have already been proposed in the literature. But such solutions may
lack scalability and suffer from inherent drawbacks of centralization, such as
a single point of failure, and data privacy concerns. Decentralization can help
tackle these challenges. In this paper, a fully decentralized smart charging
system is proposed using the philosophy of adaptive multi-agent systems. The
proposed system utilizes multi-armed bandit learning to handle uncertainties in
the system. The presented system is decentralized, scalable, real-time,
model-free, and takes fairness among different players into account. A detailed
case study is also presented for performance evaluation.

提出了一种基于自适应多智能体系统哲学的完全去中心化智能充电系统，利用多臂赌博学习来处理系统中的不确定性，并考虑了不同参与者的公平性，通过详细案例研究进行了性能评估。